Aims. The aim of this study is to compare our results of preoperative chemotherapy followed by pancreaticoduodenectomy (PD) with those of surgery alone in patients with localized resectable pancreatic ductal adenocarcinoma (PDAC). Methods. Outcome data for 112 patients of resectable PDAC who received preoperative chemoradiotherapy followed by PD (group I) between January 2004 and April 2010 were retrospectively analyzed and were compared with selected 120 patients who underwent PD alone (group II) in the same period. Results. Patients in group I had an incidence of locoregional recurrence of 17.1% compared to 30.8% in group II (P = 0.03). There were no statistically significant differences in postoperative morbidity (27.7% versus 30.8%) and mortality (2.67% versus 3.33%). The 1-, 2-, and 3-year survival rates were estimated at 82.1%, 54%, and 28%, respectively, with NCRT and 65.8%, 29.1%, and 10% without (P = 0.006). Nevertheless, preoperative chemotherapy did not reduce the 1-, 3-, and 5-year disease-free survival rates, which were estimated at 58%, 36.6%, and 12.5% with NCRT and 51.7%, 18.3%, and 7.5% without (P = 0.058). Conclusions. The treatment of NCRT followed by PD in patients with PDAC has a significantly lower rate of locoregional recurrence and a longer overall survival than those with surgery alone.

The accurate and reproducible delineation of tumors from uninvolved tissue is essential for radiation oncology. However, the tumor margin may be challenging to identify from magnetic resonance (MR) images of nasopharyngeal carcinomas (NPCs). Additionally, clinical diagnoses such as T-staging may already provide some information on tumor invasion. To use this information and improve the performance of tumor segmentation, we propose a novel deep learning neural network architecture that can incorporate both T-staging and image information. Based on U-Net, our model adds a T-channel composed of T-staging information and uses the attention mechanism. Since the T-staging information is defined by the extent of tumor invasion, the T-channel using T-staging information can improve the segmentation accuracy at different stages. Additionally, the addition of an attention mechanism allows our model to retain the most valuable pixels of the image, thus further improving the delineation accuracy. In our experiments, the proposed network was trained and validated based on records from 251 clinical patients using 10-fold crossvalidation. The dice similarity coefficient (DSC) and average symmetric surface distance (ASSD) were used to evaluate our network's results. The average DSC and ASSD and their standard deviation (SD) values are 0.841 ± 0.011 and 0.747 ± 0.199 mm. The unique T-channel effectively utilizes T-staging information to improve the results. With the combination of the T-channel module and the attention module, we significantly improved NPC tumor delineation performance.

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